Adaptive Symbol Recognition for Sketch-Based Interfaces Based on Template Matching and SVM
Yingying Jiang, Feng Tian, Wang Xu-gang, Guozhong Dai · Chinese Journal of Computers · 2009
During adaptive learning of symbols in sketch-based interfaces,the number of training samples may be different for different users and it is challenging for recognition methods to learn with flexible sample numbers.This paper proposes an adaptive symbol recognition method for sketch-based interfaces.It combines template matching method that could learn with few samples and SVM method that could learn with more samples by a strategy related to sample numbers.Both online information and offline information are utilized.Thus it could learn and recognize with different sample numbers.Based on the proposed method,the authors build a symbol widget that supports adaptive recognition.At last,a prototype system,IdeaNote,is built based on the extended PIBG Toolkit.Evaluation shows that when there are 24 kinds of symbols,the method could achieve high recognition accuracy and good time performance with 1 to 20 training samples.